The costs of poor data quality

Abstract: Purpose: The
technological developments have implied that companies store increasingly more
data. However, data quality maintenance work is often neglected, and poor
quality business data constitute a significant cost factor for many companies.
This paper argues that perfect data quality should not be the goal, but instead
the data quality should be improved to only a certain level. The paper focuses
on how to identify the optimal data quality level.

Design/methodology/approach: The paper starts with a review of data
quality literature. On this basis, the paper proposes a definition of the
optimal data maintenance effort and a classification of costs inflicted by poor
quality data. These propositions are investigated by a case study.

Findings: The paper proposes: (1) a definition of the optimal data
maintenance effort and (2) a classification of costs inflicted by poor quality
data. A case study illustrates the usefulness of these propositions.

Research limitations/implications: The paper provides definitions in
relation to the costs of poor quality data and the data quality maintenance
effort. Future research may build on these definitions. To further develop the
contributions of the paper, more studies are needed.

Practical implications: As illustrated by the case study, the definitions
provided by this paper can be used for determining the right data maintenance
effort and costs inflicted by poor quality data. In many companies, such
insights may lead to significant savings.

Originality/value: The paper provides a clarification of what are the
costs of poor quality data and defines the relation to data quality maintenance
effort. This represents an original contribution of value to future research
and practice.